Object Detection Techniques: Overview and Performance Comparison

被引：3
|
作者：
Noman, Mohammed [1 ]
Stankovic, Vladimir [1 ]
Tawfik, Ayman [2 ]
机构：
[1] Univ Strathclyde, Elect & Elect Engn, Glasgow, Lanark, Scotland
[2] Ajman Univ, Engn & Informat Technol, Ajman, U Arab Emirates
来源：
2019 IEEE 19TH INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT 2019) | 2019年
关键词：
Object detection; Tensorflow; Azure Cloud;
D O I：
10.1109/isspit47144.2019.9001879
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Object detection algorithms are improving by the minute. There are many common libraries or application program interfaces (APIs) to use. The most two common ones are Microsoft Azure Cloud object detection and Google Tensorflow object detection. The first is an online-network based API, while the second is an offline machine-based API. Both have their advantages and disadvantages. A direct comparison between the most common object detection methods helps in finding the best solution for advance system integration. This paper will discuss both methods and compare them in terms of accuracy, complexity and practicality. It will show advantages and also limitations of each method, and possibilities for improvement.
引用
收藏
页数：5
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